The state of sustainable investments in key emerging markets: synthesis report

The report is intended as a summary and synthesis of country reports on the state of sustainable investing in key emerging markets, namely China, India, Brazil and Turkey. In general, the authors have defined sustainable investments as investments that incorporate environmental (E), social(S) and governance (G) factors into the investment processes. The reports primarily investigate sustainable investments through the supply of financial capital to publicly listed firms in the form of equity investments through the stock markets, using strategies that incorporate environmental, social, and governance (ESG) risks into the investment process, with a long-term perspective. These investments can be purposefully ESG-inclusive and marketed as such (theme-based or labelled), or they can include ESG factors somehow in the processes without explicitly referring to sustainability-related factors. The reports also consider the supply of financial capital in various classes and forms to listed and privately held firms with a consideration of the investment’s impact on economic and social development or on investors’ values

Sustainable investment in Turkey: issue brief

IFC launched a series of sustainable investment country reports initially covering the largest emerging capital markets attracting global portfolio investors: Brazil, India, and China. Further reports have been added to the series covering Sub-Saharan Africa, the Middle East and North Africa, and Turkey. This Issue Brief 's the summary version of the report, “Sustainable Investment in Turkey,

Sustainable investment in Turkey 2010

The main objectives of this report are as follows: 1 To understand and provide a review of the current state of the Sustainable Investment (SI) market in Turkey, 2 To identify the drivers and obstacles for sustainable investments, and assess the commercial feasibility of different approaches and initiatives that may stimulate the SI market in Turkey, 3 To analyze the institutional prerequisites and interventions that will fuel the development of investments, which would, in turn, encourage a betterallocation of local and international capital to sustainable enterprises and hence support sustainable development of the Turkish economy. This study forms part of a series of assessments of Sustainable Investment (SI) in Brazil (2009), India (2009) and China (2009), and draws upon earlier reports published by IFC jointly with the Economist Intelligence Unit: Sustainable Invest ing in Emerging Markets: Unscathed by the Financial Crises (2010) and with Mercer; Gaining Ground, Integrating Environmental, Social and Governance (ESG) Factors into Investment Processes in Emerging Markets (2009)

Vehicle-to-grid for car sharing -- A simulation study for 2030

The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these systems can play a pivotal role in bolstering grid stability through the provision of ancillary services via vehicle-to-grid (V2G) technologies - a facet that has received limited attention in previous research. In this study, we analyze the potential of V2G in car sharing by designing future scenarios for a national-scale service in Switzerland. We propose an agent-based simulation pipeline that considers population changes as well as different business strategies of the car sharing service, and we demonstrate its successful application for simulating scenarios for 2030. To imitate car sharing user behavior, we develop a data-driven mode choice model. Our analysis reveals important differences in the examined scenarios, such as higher vehicle utilization rates for a reduced fleet size as well as in a scenario featuring new car sharing stations. These disparities translate into variations in the power flexibility of the fleet available for ancillary services, ranging from 12 to 50 MW, depending on the scenario and the time of the day. Furthermore, we conduct a case study involving a subset of the car sharing fleet, incorporating real-world electricity pricing data. The case study substantiates the existence of a sweet spot involving monetary gains for both power grid operators and fleet owners. Our findings provide guidelines to decision makers and underscore the pressing need for regulatory enhancements concerning power trading within the realm of car sharing

Do poverty and wealth look the same the world over? A comparative study of 12 cities from five high-income countries using street images

Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world

Contributing to a sustainable stock exchange: CDP Turkey 100 climate change report 2013

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